Original scientific paper

https://doi.org/10.21278/TOF.483062023

Efficient Path Planning for Drilling Processes: The Hybrid Approach of a Genetic Algorithm and Ant Colony Optimisation

Kursat Tanriver orcid.org/0000-0002-1723-4108 ; Faculty of Engineering and Natural Sciences, Department of mechatronics Engineering, Istanbul Health and Technology University, Istanbul, Turkey *
Mustafa Ay orcid.org/0000-0002-7672-1846 ; Faculty of Technology, Mechanical Engineering Department, Marmara University, Istanbul, Turkey

* Corresponding author.

Abstract

Efficiency in machining time during drilling is affected by various factors, with one key element being the machining path. Solving the machining path closely resembles the Travelling Salesman Problem (TSP). In this article, drilling on a sample model is simulated using a hybrid algorithm that is developed based on TSP. This hybrid algorithm (GACO) is created by combining the strengths of the Genetic Algorithm (GA) and Ant Colony Optimisation (ACO). Codes written to verify the stability of the algorithms were executed 10 times, and results were recorded indicating the shortest path and machining sequence. Accordingly, the performance of the hybrid GACO algorithm was observed to be 3.16% better than the ACO algorithm in terms of both total path length and total machining time. In terms of computation time, the ACO algorithm lagged behind the GACO algorithm by 6.46%. Furthermore, the hybrid GACO algorithm demonstrated enhanced performance in both total path length and total machining time when compared with the literature. This study aims to contribute to the industry, professionals, and practitioners in this field by providing cost and time savings.

Keywords

ant colony; drilling; machining; tool pathing optimisation; travelling salesman person

Hrčak ID:

319759

URI

https://hrcak.srce.hr/319759

Publication date:

19.6.2024.

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